Deep neural networks are used heavily on mobile devices for a variety of tasks, including scene detection, facial recognition, image sorting and labeling. Convolution is frequently used for deep neural networks to accomplish these tasks, and is usually implemented using matrix multiplication. Deep neural network models are trained for floating point computations. On mobile devices, deep neural network models, such as prediction models, are now also using fixed point computations. However, many implementations of deep neural network models using fixed point computations require use of extra amounts of memory that reduces performance speeds of the mobile device.